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Guni Sharon, James Ault, Peter Stone, Varun Kompella, and Roberto Capobianco. Multiagent Epidemiologic Inference through Realtime Contact Tracing. In Proceedings of the 20th International Conference on Autonomous Agents and MultiAgent Systems (AAMAS 2021), May 2021.
This paper addresses an epidemiologic inference problem where, given realtime observation of test results, presence of symptoms, and physical contacts, the most likely infected individuals need to be inferred. The inference problem is modeled as a hidden Markov model where infection probabilities are updated at every time step and evolve between time steps. We suggest a unique inference approach that avoids storing the given observations explicitly. Theoretical justification for the proposed model is provided under specific simplifying assumptions. To complement these theoretical results, a comprehensive experimental study is performed using a custom-built agent-based simulator that models inter-agent contacts. The reported results show the effectiveness of the proposed inference model when considering more realistic scenarios -- where the simplifying assumptions do not hold. When pairing the proposed inference model with a simple testing and quarantine policy, promising trends are obtained where the epidemic progression is significantly slowed down while quarantining a bounded number of individuals.
@inproceedings{AAMAS21_COVID,
title={Multiagent Epidemiologic Inference through Realtime Contact Tracing},
author={Guni Sharon and James Ault and Peter Stone and Varun Kompella and Roberto Capobianco},
booktitle={Proceedings of the 20th International Conference on Autonomous Agents and MultiAgent Systems (AAMAS 2021)},
location = {London, UK},
month = {May},
year={2021},
organization={International Foundation for Autonomous Agents and Multiagent Systems},
abstract = {
This paper addresses an epidemiologic inference problem where,
given realtime observation of test results, presence of symptoms,
and physical contacts, the most likely infected individuals need
to be inferred. The inference problem is modeled as a hidden
Markov model where infection probabilities are updated at every
time step and evolve between time steps. We suggest a unique
inference approach that avoids storing the given observations
explicitly. Theoretical justification for the proposed model is
provided under specific simplifying assumptions. To complement
these theoretical results, a comprehensive experimental study is
performed using a custom-built agent-based simulator that models
inter-agent contacts. The reported results show the effectiveness
of the proposed inference model when considering more realistic
scenarios -- where the simplifying assumptions do not hold. When
pairing the proposed inference model with a simple testing and
quarantine policy, promising trends are obtained where the
epidemic progression is significantly slowed down while
quarantining a bounded number of individuals.},
}
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